However, multi-tracer brain PET is challenging to deploy in real-world scenarios due to the radiation dose to both patient and healthcare providers, as well as the increasing cost when more tracer studies are involved. The ability to synthesize multi-tracer PET images from single-tracer PET images is paramount in terms of providing more information for assessing AD with minimal radiation dose and cost. As \(^{11}\) C-UCB-J reflects the neural synaptic density, \(^{18}\) F-FDG reflects the cell metabolism (high activities at the neural synapse) and \(^{11}\) C-PiB reflecting the neural growth/repair activities, the PET images from these three tracers are correlated which post the possibility to predict one tracer image from another tracer image. With the recent advances in deep learning based image translation techniques [1]}, [2]}, these techniques have been widely utilized in medical imaging for translation between individual modalities and individual acquisition protocols, such as MR-PET [3]}, DR-DE [4]}, and PET-PET [5]}. Even though similar strategies can be utilized for synthesizing multi-tracer PET images from single-tracer PET image, it will result in the need for training large amount of one-to-one domain translation model (\(\mathcal {A}_n^2=n \times (n-1)\) ) and leaving features learned from multiple domain under-utilized. Let \(^{18}\) F-FDG, \(^{11}\) C-UCB-J, and \(^{11}\) C-PiB be denoted as PET tracer A, B, and C, respectively. Given the three PET tracers scenario, traditional methods require \(\mathcal {A}_3^2 = 6\)  translation models for translating \(A \rightarrow B\)  / \(A \rightarrow C\)  / \(B \rightarrow A\)  / \(B \rightarrow C\)  / \(C \rightarrow A\)  / \(C \rightarrow B\) . In addition, brain PET tracer images, as functional images, are challenging to translate between tracer domain without prior knowledge of anatomy.
